Risk-Based Completion Cost Overrun Ratio Estimation in Construction Projects Using Machine Learning Classification Algorithms: A Case Study

Abstract

Estimating the completion cost accurately in the early phases of construction projects is critical to their success. However, cost overruns are almost inevitable due to the risks inherent in construction projects. Hence, the completion cost fluctuates throughout the execution phase and requires periodic updates. There is a need for a prompt and user-friendly completion cost estimation model that accounts for fluctuating risk scores and their impacts on the total cost during the execution phase. Machine learning (ML) techniques could address these requirements by providing effective methods for tackling dynamic systems. The proposed approach aims to predict the cost overrun ratio classes of the completion cost according to the changes in the total risk scores at any time of the project. Six classification algorithms were utilized and validated by employing 110 data points from a globally operating construction company. The performances of the algorithms were evaluated with validation and performance indices. The decision tree classifier surpassed other algorithms. Although there are some research limitations, including risk perception, data gathering restrictions, and selecting proper ML algorithms upon data properties, this research improves the planning abilities of construction executives by providing a cost overrun ratio based on changing total risk scores, facilitating swift and simple assessments at any stage of a construction project’s execution.